Confirmatory Factor Analysis -- A Case study

TL;DR

This paper discusses confirmatory factor analysis (CFA), introduces advanced R-based techniques for conducting CFA, and demonstrates their application through examples and datasets to assess model fit in social research.

Contribution

It presents novel state-of-the-art methods for performing CFA in R and illustrates their use with practical examples and datasets.

Findings

Effective CFA techniques demonstrated in R

Multiple scenarios where CFA is applicable

Insights into model fit assessment

Abstract

Confirmatory Factor Analysis (CFA) is a particular form of factor analysis, most commonly used in social research. In confirmatory factor analysis, the researcher first develops a hypothesis about what factors they believe are underlying the used measures and may impose constraints on the model based on these a priori hypotheses. For example, if two factors are accounting for the covariance in the measures, and these factors are unrelated to one another, we can create a model where the correlation between factor X and factor Y is set to zero. Measures could then be obtained to assess how well the fitted model captured the covariance between all the items or measures in the model. Thus, if the results of statistical tests of the model fit indicate a poor fit, the model will be rejected. If the fit is weak, it may be due to a variety of reasons. We propose to introduce state of the art techniques to do CFA in R language. Then, we propose to do some examples of CFA with R and some datasets, revealing several scenarios where CFA is relevant.